An Agent Model for Information Filtering using Revolutionary RSVD Technique
Dussadee Praserttitipong [a] * and Peraphon Sophatsathit [b]* Author for corresponding; e-mail address: speraphon@gmail.com; dussadee.p@cmu.ac.th
Volume: Vol.41 No.5/2 (OCTOBER 2014)
Research Article
DOI:
Received: 20 March 2012, Revised: -, Accepted: 11 July 2013, Published: -
Citation: Praserttitipong D. and Sophatsathit P., An Agent Model for Information Filtering using Revolutionary RSVD Technique , Chiang Mai Journal of Science, 2014; 41(5/2): 1429-1438.
Abstract
This paper proposes a collaborative software agent model. The agent works in a distributed environment making recommendation based on its up-to-date knowledge. This knowledge is partly acquired from other collaborative agents to combine with its own prior knowledge by means of a revolutionary regularized singular value decomposition (rRSVD) technique. The technique is used as an adaptation process for the agent to learn and update the knowledge periodically. This process employs one of the three agent adaptation models, namely, 2-phase, 1-phase, or non-adaptation that is suitable for the operating bandwidth, along with a fast incremental knowledge adaptation algorithm. As a consequence, the adapted agent will be able to work alone in a distributed environment at a satisfactorily level of performance.